OpenRaiser/Intern-Atlas huggingface dataset:
Merging Continual Pretraining Models for Domain-Specialized LLMs: A Case Study in Finance
Venue: arXiv cs.CL 2025
Year: 2025

While LLMs excel at general tasks, they struggle in specialized domains like finance, requiring diverse skills in domain knowledge, mathematical reasoning, and multilingual processing. Merging domain-specific Continual Pre-training (CPT) "experts" offers a practical alternative to costly and unstable multi-skill training. However, unlike established Supervised Fine-Tuning (SFT) model-based merging, CPT model merging remains largely unexplored. We address this gap by creating financial LLMs from experts in finance, math, and Japanese. We propose a three-stage evaluation focusing on knowledge recovery, complementarity, and emergence, and assess three merging methods (Task Arithmetic, TIES, and DARE-TIES) on a comprehensive financial benchmark curated from 18 tasks across 8 established datasets. Results show that merging an expert with its base model recovers general knowledge lost during CPT, while merging experts improves performance and can yield emergent cross-domain skills. Among the methods, Task Arithmetic performs strongly but is hyperparameter-sensitive, whereas TIES is more robust. Our findings also suggest that while model similarity correlates with merging success, emergent skills depend on more complex factors. This work presents the first foundational analysis of CPT model merging, establishing a principled framework and providing clear guidance for building multi-skill LLMs from existing assets.
Lost in Code Generation: Reimagining the Role of Software Models in AI-driven Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

Generative AI enables rapid ``vibe coding," where natural language prompts yield working software systems. While this lowers barriers to software creation, it also collapses the boundary between prototypes and engineered software, leading to fragile systems that lack robustness, security, and maintainability. We argue that this shift motivates a reimagining of software models. Rather than serving only as upfront blueprints, models can be recovered post-hoc from AI-generated code to restore comprehension, expose risks, and guide refinement. In this role, models serve as mediators between human intent, AI generation, and long-term system evolution, providing a path toward sustainable AI-driven software engineering.
NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers
Venue: arXiv cs.LG 2025
Year: 2025

Partial differential equations (PDEs) underpin quantitative descriptions across the physical sciences and engineering, yet high-fidelity simulation remains a major computational bottleneck for many-query, real-time, and design tasks. Data-driven surrogates can be strikingly fast but are often unreliable when applied outside their training distribution. Here we introduce Neural Operator Warm Starts (NOWS), a hybrid strategy that harnesses learned solution operators to accelerate classical iterative solvers by producing high-quality initial guesses for Krylov methods such as conjugate gradient and GMRES. NOWS leaves existing discretizations and solver infrastructures intact, integrating seamlessly with finite-difference, finite-element, isogeometric analysis, finite volume method, etc. Across our benchmarks, the learned initialization consistently reduces iteration counts and end-to-end runtime, resulting in a reduction of the computational time of up to 90 %, while preserving the stability and convergence guarantees of the underlying numerical algorithms. By combining the rapid inference of neural operators with the rigor of traditional solvers, NOWS provides a practical and trustworthy approach to accelerate high-fidelity PDE simulations.
Generalized informational functionals and new monotone measures of statistical complexity
Venue: arXiv cs.IT 2025
Year: 2025

In this paper we introduce a biparametric family of transformations which can be seen as an extension of the so-called up and down transformations. This new class of transformations allows to us to introduce new informational functionals, which we have called \textit{down-moments} and \textit{cumulative upper-moments}. A remarkable fact is that the down-moments provide, in some cases, an interpolation between the $p$-th moments and the power Rényi entropies of a probability density. We establish new and sharp inequalities relating these new functionals to the classical informational measures such as moments, Rényi and Shannon entropies and Fisher information measures. We also give the optimal bounds as well as the minimizing densities, which are in some cases expressed in terms of the generalized trigonometric functions. We furthermore define new classes of measures of statistical complexity obtained as quotients of the new functionals, and establish monotonicity properties for them through an algebraic conjugation of up and down transformations. All of these properties highlight an intricate structure of functional inequalities.
ESA: Energy-Based Shot Assembly Optimization for Automatic Video Editing
Venue: arXiv cs.CV 2025
Year: 2025

Shot assembly is a crucial step in film production and video editing, involving the sequencing and arrangement of shots to construct a narrative, convey information, or evoke emotions. Traditionally, this process has been manually executed by experienced editors. While current intelligent video editing technologies can handle some automated video editing tasks, they often fail to capture the creator's unique artistic expression in shot assembly. To address this challenge, we propose an energy-based optimization method for video shot assembly. Specifically, we first perform visual-semantic matching between the script generated by a large language model and a video library to obtain subsets of candidate shots aligned with the script semantics. Next, we segment and label the shots from reference videos, extracting attributes such as shot size, camera motion, and semantics. We then employ energy-based models to learn from these attributes, scoring candidate shot sequences based on their alignment with reference styles. Finally, we achieve shot assembly optimization by combining multiple syntax rules, producing videos that align with the assembly style of the reference videos. Our method not only automates the arrangement and combination of independent shots according to specific logic, narrative requirements, or artistic styles but also learns the assembly style of reference videos, creating a coherent visual sequence or holistic visual expression. With our system, even users with no prior video editing experience can create visually compelling videos. Project page: https://sobeymil.github.io/esa.com
Keeping it Local, Tiny and Real: Automated Report Generation on Edge Computing Devices for Mechatronic-Based Cognitive Systems
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in Deep Learning enable hardware-based cognitive systems, that is, mechatronic systems in general and robotics in particular with integrated Artificial Intelligence, to interact with dynamic and unstructured environments. While the results are impressive, the application of such systems to critical tasks like autonomous driving as well as service and care robotics necessitate the evaluation of large amount of heterogeneous data. Automated report generation for Mobile Robotics can play a crucial role in facilitating the evaluation and acceptance of such systems in various domains. In this paper, we propose a pipeline for generating automated reports in natural language utilizing various multi-modal sensors that solely relies on local models capable of being deployed on edge computing devices, thus preserving the privacy of all actors involved and eliminating the need for external services. In particular, we evaluate our implementation on a diverse dataset spanning multiple domains including indoor, outdoor and urban environments, providing quantitative as well as qualitative evaluation results. Various generated example reports and other supplementary materials are available via a public repository.
LiteVoxel: Low-memory Intelligent Thresholding for Efficient Voxel Rasterization
Venue: arXiv cs.CV 2025
Year: 2025

Sparse-voxel rasterization is a fast, differentiable alternative for optimization-based scene reconstruction, but it tends to underfit low-frequency content, depends on brittle pruning heuristics, and can overgrow in ways that inflate VRAM. We introduce LiteVoxel, a self-tuning training pipeline that makes SV rasterization both steadier and lighter. Our loss is made low-frequency aware via an inverse-Sobel reweighting with a mid-training gamma-ramp, shifting gradient budget to flat regions only after geometry stabilize. Adaptation replaces fixed thresholds with a depth-quantile pruning logic on maximum blending weight, stabilized by EMA-hysteresis guards and refines structure through ray-footprint-based, priority-driven subdivision under an explicit growth budget. Ablations and full-system results across Mip-NeRF 360 (6scenes) and Tanks & Temples (3scenes) datasets show mitigation of errors in low-frequency regions and boundary instability while keeping PSNR/SSIM, training time, and FPS comparable to a strong SVRaster pipeline. Crucially, LiteVoxel reduces peak VRAM by ~40%-60% and preserves low-frequency detail that prior setups miss, enabling more predictable, memory-efficient training without sacrificing perceptual quality.
An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems
Venue: arXiv cs.LG 2025
Year: 2025

The capacitated location-routing problems (CLRPs) are classical problems in combinatorial optimization, which require simultaneously making location and routing decisions. In CLRPs, the complex constraints and the intricate relationships between various decisions make the problem challenging to solve. With the emergence of deep reinforcement learning (DRL), it has been extensively applied to address the vehicle routing problem and its variants, while the research related to CLRPs still needs to be explored. In this paper, we propose the DRL with heterogeneous query (DRLHQ) to solve CLRP and open CLRP (OCLRP), respectively. We are the first to propose an end-to-end learning approach for CLRPs, following the encoder-decoder structure. In particular, we reformulate the CLRPs as a markov decision process tailored to various decisions, a general modeling framework that can be adapted to other DRL-based methods. To better handle the interdependency across location and routing decisions, we also introduce a novel heterogeneous querying attention mechanism designed to adapt dynamically to various decision-making stages. Experimental results on both synthetic and benchmark datasets demonstrate superior solution quality and better generalization performance of our proposed approach over representative traditional and DRL-based baselines in solving both CLRP and OCLRP.
Redundancy Maximization as a Principle of Associative Memory Learning
Venue: arXiv cs.IT 2025
Year: 2025

Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues. Yet, the local computational principles which are required to enable this function remain incompletely understood. To formally characterize the local information processing in such systems, we employ a recent extension of information theory - Partial Information Decomposition (PID). PID decomposes the contribution of different inputs to an output into unique information from each input, redundant information across inputs, and synergistic information that emerges from combining different inputs. Applying this framework to individual neurons in classical Hopfield networks we find that below the memory capacity, the information in a neuron's activity is characterized by high redundancy between the external pattern input and the internal recurrent input, while synergy and unique information are close to zero until the memory capacity is surpassed and performance drops steeply. Inspired by this observation, we use redundancy as an information-theoretic learning goal, which is directly optimized for each neuron, dramatically increasing the network's memory capacity to 1.59, a more than tenfold improvement over the 0.14 capacity of classical Hopfield networks and even outperforming recent state-of-the-art implementations of Hopfield networks. Ultimately, this work establishes redundancy maximization as a new design principle for associative memories and opens pathways for new associative memory models based on information-theoretic goals.
The Limit of Recursion in State-based Systems
Venue: arXiv cs.LO 2025
Year: 2025

We prove that omega^2 strictly bounds the iterations required for modal definable functions to reach a fixed point across all countable structures. The result corrects and extends the previously claimed result by the first and third authors on closure ordinals of the alternation-free mu-calculus in [3]. The new approach sees a reincarnation of Kozen's well-annotations, devised for showing the finite model property for the modal mu-calculus. We develop a theory of 'conservative' well-annotations where minimality of annotations is guaranteed, and isolate parts of the structure that locally determine the closure ordinal of relevant formulas. This adoption of well-annotations enables a direct and clear pumping process that rules out closure ordinals between omega^2 and the limit of countability.
On The Dangers of Poisoned LLMs In Security Automation
Venue: arXiv cs.CR 2025
Year: 2025

This paper investigates some of the risks introduced by "LLM poisoning," the intentional or unintentional introduction of malicious or biased data during model training. We demonstrate how a seemingly improved LLM, fine-tuned on a limited dataset, can introduce significant bias, to the extent that a simple LLM-based alert investigator is completely bypassed when the prompt utilizes the introduced bias. Using fine-tuned Llama3.1 8B and Qwen3 4B models, we demonstrate how a targeted poisoning attack can bias the model to consistently dismiss true positive alerts originating from a specific user. Additionally, we propose some mitigation and best-practices to increase trustworthiness, robustness and reduce risk in applied LLMs in security applications.
CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self-consistency strategy (arXiv:2203.11171) requires a fixed number of calls and can fail when the correct answer is rare. We introduce Confidence-Guided Early Stopping (CGES), a Bayesian framework that forms posteriors over candidate answers using scalar confidence signals derived from token probabilities or reward models. CGES adaptively halts sampling once the posterior mass of a candidate exceeds a threshold. We provide theoretical guarantees for both perfectly calibrated confidences and realistic noisy confidence signals. Across five reasoning benchmarks, CGES reduces the average number of model calls by about 69 percent (for example, from 16.0 to 4.9) while matching the accuracy of self-consistency within 0.06 percentage points.
Community Notes are Vulnerable to Rater Bias and Manipulation
Venue: arXiv cs.SI 2025
Year: 2025

Social media platforms increasingly rely on crowdsourced moderation systems like Community Notes to combat misinformation at scale. However, these systems face challenges from rater bias and potential manipulation, which may undermine their effectiveness. Here we systematically evaluate the Community Notes algorithm using simulated data that models realistic rater and note behaviors, quantifying error rates in publishing helpful versus unhelpful notes. We find that the algorithm suppresses a substantial fraction of genuinely helpful notes and is highly sensitive to rater biases, including polarization and in-group preferences. Moreover, a small minority (5--20\%) of bad raters can strategically suppress targeted helpful notes, effectively censoring reliable information. These findings suggest that while community-driven moderation may offer scalability, its vulnerability to bias and manipulation raises concerns about reliability and trustworthiness, highlighting the need for improved mechanisms to safeguard the integrity of crowdsourced fact-checking.
Decentralized AI Service Placement, Selection and Routing in Mobile Networks
Venue: arXiv cs.NI 2025
Year: 2025

The rapid development and usage of large-scale AI models by mobile users will dominate the traffic load in future communication networks. The advent of AI technology also facilitates a decentralized AI ecosystem where small organizations or even individuals can host AI services. In such scenarios, AI service (models) placement, selection, and request routing decisions are tightly coupled, posing a challenging yet fundamental trade-off between service quality and service latency, especially when considering user mobility. Existing solutions for related problems in mobile edge computing (MEC) and data-intensive networks fall short due to restrictive assumptions about network structure or user mobility. To bridge this gap, we propose a decentralized framework that jointly optimizes AI service placement, selection, and request routing. In the proposed framework, we use traffic tunneling to support user mobility without costly AI service migrations. To account for nonlinear queuing delays, we formulate a nonconvex problem to optimize the trade-off between service quality and end-to-end latency. We derive the node-level KKT conditions and develop a decentralized Frank--Wolfe algorithm with a novel messaging protocol. Numerical evaluations validate the proposed approach and show substantial performance improvements over existing methods.
Bringing Private Reads to Hyperledger Fabric via Private Information Retrieval
Venue: arXiv cs.CR 2025
Year: 2025

Permissioned blockchains ensure integrity and auditability of shared data but expose query parameters to peers during read operations, creating privacy risks for organizations querying sensitive records. This paper proposes a Private Information Retrieval (PIR) mechanism to enable private reads from Hyperledger Fabric's world state, allowing endorsing peers to process encrypted queries without learning which record is accessed. We implement and benchmark a PIR-enabled chaincode that performs ciphertext-plaintext (ct-pt) homomorphic multiplication directly within evaluate transactions, preserving Fabric's endorsement and audit semantics. The prototype achieves an average end-to-end latency of 113 ms and a peer-side execution time below 42 ms, with approximately 2 MB of peer network traffic per private read in development mode--reducible by half under in-process deployment. Storage profiling across three channel configurations shows near-linear growth: block size increases from 77 kilobytes to 294 kilobytes and world-state from 112 kilobytes to 332 kilobytes as the ring dimension scales from 8,192 to 32,768 coefficients. Parameter analysis further indicates that ring size and record length jointly constrain packing capacity, supporting up to 512 records of 64 bytes each under the largest configuration. These results confirm the practicality of PIR-based private reads in Fabric for smaller, sensitive datasets and highlight future directions to optimize performance and scalability.
EasyTUS: A Comprehensive Framework for Fast and Accurate Table Union Search across Data Lakes
Venue: arXiv cs.DB 2025
Year: 2025

Data lakes enable easy maintenance of heterogeneous data in its native form. While this flexibility can accelerate data ingestion, it shifts the complexity of data preparation and query processing to data discovery tasks. One such task is Table Union Search (TUS), which identifies tables that can be unioned with a given input table. In this work, we present EasyTUS, a comprehensive framework that leverages Large Language Models (LLMs) to perform efficient and scalable Table Union Search across data lakes. EasyTUS implements the search pipeline as three modular steps: Table Serialization for consistent formatting and sampling, Table Representation that utilizes LLMs to generate embeddings, and Vector Search that leverages approximate nearest neighbor indexing for semantic matching. To enable reproducible and systematic evaluation, in this paper, we also introduce TUSBench, a novel standardized benchmarking environment within the EasyTUS framework. TUSBench supports unified comparisons across approaches and data lakes, promoting transparency and progress in the field. Our experiments using TUSBench show that EasyTUS consistently outperforms most of the state-of the-art approaches, achieving improvements in average of up to 34.3% in Mean Average Precision (MAP), up to 79.2x speedup in data preparation, and up to 7.7x faster query processing performance. Furthermore, EasyTUS maintains strong performance even in metadata-absent settings, highlighting its robustness and adaptability across data lakes.
Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) exhibit complementary strengths across domains and come with varying inference costs, motivating the design of multi-agent LLM systems where specialized models collaborate efficiently. Existing approaches predominantly rely on decentralized frameworks, which invoke multiple LLMs for every input and thus lead to substantial and uncontrolled inference costs. In this work, we introduce a centralized multi-LLM framework, where a controller LLM selectively coordinates a pool of expert models in a cost-efficient and cost-controllable manner. We formulate this coordination problem as reinforcement learning with dual objectives: maximizing task performance while minimizing the overall inference cost. In addition, we expect the multi-agent system to have adapted behavior with different budget conditions during inference. To this end, we propose CoRL, a reinforcement learning framework that optimizes the performance cost trade-off in a controllable multi-budget setting. Experiments on four diverse benchmarks demonstrate that CoRL enables a single system to surpass the best expert LLM under high-budget settings, while maintaining strong performance in more economical low-budget modes, highlighting the effectiveness of centralized coordination for scalable and cost-efficient multi-agent LLM systems.
From Solo to Symphony: Orchestrating Multi-Agent Collaboration with Single-Agent Demos
Venue: arXiv cs.LG 2025
Year: 2025

Training a team of agents from scratch in multi-agent reinforcement learning (MARL) is highly inefficient, much like asking beginners to play a symphony together without first practicing solo. Existing methods, such as offline or transferable MARL, can ease this burden, but they still rely on costly multi-agent data, which often becomes the bottleneck. In contrast, solo experiences are far easier to obtain in many important scenarios, e.g., collaborative coding, household cooperation, and search-and-rescue. To unlock their potential, we propose Solo-to-Collaborative RL (SoCo), a framework that transfers solo knowledge into cooperative learning. SoCo first pretrains a shared solo policy from solo demonstrations, then adapts it for cooperation during multi-agent training through a policy fusion mechanism that combines an MoE-like gating selector and an action editor. Experiments across diverse cooperative tasks show that SoCo significantly boosts the training efficiency and performance of backbone algorithms. These results demonstrate that solo demonstrations provide a scalable and effective complement to multi-agent data, making cooperative learning more practical and broadly applicable.